Presenters-0635: Difference between revisions
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|CSDMS meeting first name co1= | |CSDMS meeting first name co1=Pratikshya | ||
|CSDMS meeting last name co1= | |CSDMS meeting last name co1=Regmi | ||
|CSDMS meeting institute co1=NC State | |CSDMS meeting institute co1=NC State Center for Geospatial Analytics | ||
|CSDMS meeting country co1=United States | |CSDMS meeting country co1=United States | ||
|CSDMS meeting state co1=North Carolina | |CSDMS meeting state co1=North Carolina | ||
|CSDMS meeting email address co1= | |CSDMS meeting email address co1=pregmi3@ncsu.ed | ||
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|CSDMS meeting email address co1=hmitaso@ncsu.edu | |CSDMS meeting email address co1=hmitaso@ncsu.edu | ||
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|CSDMS meeting first name co1=Anna | |||
|CSDMS meeting last name co1=Petrasova | |||
|CSDMS meeting institute co1=NC State University, Center for Geospatial Analytics | |||
|CSDMS meeting country co1=United States | |||
|CSDMS meeting state co1=North Carolina | |||
|CSDMS meeting email address co1=akratoc@ncsu.edu | |||
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Latest revision as of 10:46, 29 January 2024
CSDMS 2024: Coastlines, Critical Zones and Cascading Hazards: Modeling Dynamic Interfaces from Deep Time to Human Time
Coastal evolution analysis and inundation modeling with GRASS GIS
Abstract
This clinic will introduce participants to GRASS GIS tools with a focus on applications for coastal hazards analysis including flooding and coastal evolution. We will explain and practice GRASS GIS data management and concepts, and demonstrate them on examples of efficient LiDAR point cloud, raster, and vector data processing.
The clinic will begin with a brief introduction to the GRASS GIS software and continue with a hands-on tutorial exploring coastal evolution through a LiDAR timeseries of Bald Head Island in North Carolina, USA. Finally, we will explore some of the inundation and flood modeling tools available in GRASS GIS. The tutorial will be formatted in a series of Jupyter Notebooks executed in a cloud-based (or locally installed) JupyterLab environment, taking advantage of the latest GRASS GIS Python features for Jupyter, including 2D, 3D, webmap, and temporal visualizations.
By the end of the clinic, participants will have hands-on experience with:
- Setting up GRASS GIS Projects and importing data
- Creating high-quality DEMs from LiDAR point clouds and computing topographic parameters
- Deriving shorelines from the DEMs
- Animating changes in topography over time and computing erosion rates
- Generating simplified storm surge inundation timeseries
Please acknowledge the original contributors when you are using this material. If there are any copyright issues, please let us know (CSDMSweb@colorado.edu) and we will respond as soon as possible.
Of interest for:
